A great Friday diversion. If you company is pondering how or why to use social data to enhance analytics understanding its impact is critical. This video does a nice job driving home the key points of how pervasive social is in our lives.

Power users and self service line of business (LOB) users are always asking for more data access and easier to use tools that enable them to bypass traditional IT in favor of analytic freedom. Most of these users will push back hard on an environment that restricts thier ability to wander about in enterprise data.

Providing power and freedom to these users has incridible upside.

Faster time to insight

Less work for IT

Aligning business knowledge with the analytic process

pluggin more team members into data driven insights

These are just a few of the positives that come from self service or discovery based business intelligence. So what are the pitfalls of all of this freedom? I see two major disconnects that need to be addressed as this type of BI beomed more pervasive.

Governance - Adding desktop data is a common choice for self service users. They often mash up information from a wide variety of sources many of which are not avialble to the rest of the enterprise. This can enhance or detract from the overall analysys and leave many in the dark as to how certain decisions are being made.

Enterprise Value - The insights created by LOB and power users can be extremelly valuable to the rest of a team or the company in general. Self service solutions that don't supply a common and easy path to share and leverage these insights is missing a key feature. I am partial to solutions that balance freedom and control.

So the question is where does the control belong and how tight should it be. Does some governance and control add to the value? I think it does but the challenge is finding the sweet spot between freedom and a walled garden. If you want adoption you can't fence the power users in.

These Big Data Laws are written for the entertainment and perhaps education of vendors who are in this market and are briefing the analyst community about their solution. I Hope it helps and makes you laugh just a little bit.

Law #1 - The heavier the marketing message the lighter the technology.

Companies that lead with buzz words and marketing blather generally are struggling to deliver on the technology front. Decide early if you want to sell a marketing message or an innovative technology that will solve enterprise challenges. Sell the value not the message and stay away from marketing slogans like - Hadoop is Free!! (My POV - Its free like a puppy.)

Law #2 - The proper answer to the question "How many customers do you have?" is a numeric value.

The improper answer is anything that doesn't start with a number. The worst answer is a long convoluted narritive on how you are serving the needs of many industry segments while focusing on premier client penetration thru value added partner channels within high opportunity niche markets...blah blah blah. If you can't or won't provide a number I already know its less than 10 and I'm nervous it might be zero. I can't recommend you to my clients if I think they might become an experiment.

Law #3 - You are not the first, you are not the only and yes...you do have competitors.

Statements like these make analysts crazy and we come away thinking that you don't really understand the competitive terrain or the market in general. Steer clear of these types of declarations and focus on how you provide value and solve real business problems. (See Law #1 for clarification)

The size of today's data is old news. I already know what a Petabyte, Zettabyte and a Yottabyte are. I know about machine data, dark data, The Internet of Things, social data and sensor data. Big Data is about opportunities, being able to do workloads we could only dream of doing years ago at a speed and economic level that now makes it practicle. Educate us on what your company does and how you do it, lets skip the part where you explain how the world produces more data daily than the contents of the Library of Congress.

Law #5 - A connector to Hive is not a comprehensive Big Data strategy.

Hive is an interesting access point to Hadoop data, the ability to pass SQL into Hive opens the door for some interesting functionality but its not a comprehensive Big Data feature set. Hive is the low hanging fruit of Hadoop interaction and was the starting point for many vendors who needed/wanted to add a Big Data marketing message to their go-to-market strategy. (See Law #1 for clarification)